Patient safety is of paramount concern in all disciplines of medicine. Surgical educators and licensing boards contribute to patient safety by ensuring adequate technical training of surgical residents. Recent changes to residency requirements by the accreditation Council for Graduate Medical Education (ACGME) mandate a paradigm shift from apprenticeship-style, unsystematic training to competency-based training with objective surgical skills assessment. To date, valid, unbiased, automated skills assessment tools are used primarily to assess skill in structured settings. We hypothesize that these methods are robust under live surgery conditions and therefore can be used to directly measure a surgeon's skill while performing surgery. We further hypothesize that we will be able to distinguish between four levels of surgical skill (beginner, intermediate, advanced, and expert) and that we will be able to determine procedure-specific learning curves. Learning curves, i.e., relationships between skill and case number, are of significant interest to educators; we conjecture they will play a major role during the transition between case number and time based training to competency based training paradigms. The methods presented are general, and proof of concept will be performed in a septoplasty surgery model. Septoplasty is considered an index case for all otolaryngology residents; the complexity of the surgery makes it an ideal proto-candidate. The core scientific work in this project is to 1) create an objective skills assessment platform for developing and validating intra-operative skills assessment tools, 2) demonstrate that deterministic models of surgery useful for skills assessment exist and give insight into the surgical process, 3) validate a deterministic and stochastic (Hidden Markov Model) surgery tool for objective skills assessment and 4) measure the learning curve for septal surgery. This project is relevant to public health by ensuring patient safety through improving technical competency of surgeons.